Achieving and sustaining energy savings depends on advances in energy management (EM)–for example, the ability to exploit occupancy, energy prices, and weather trends when optimizing building system performance. Reducing the computational complexity of building-wide EM systems will make them more desirable.

Working with BuildingIQ, an Australian building automation start-up firm, Argonne is developing a proactive EM system to address these challenges. While building operations are inherently dynamic, with changes occurring quickly in external conditions, building systems respond slowly to these changes and provide an opportunity to optimize energy use, occupant comfort, and system responsiveness. To capture that opportunity, Argonne’s system will use adaptive building-wide predictive models that account for zone topology, energy and mass balances, HVAC systems, real-time occupancy, energy prices, and weather forecast information.

EM systems rely on iterative solutions of complex optimization problems, which need to be solved quickly and reliably to ensure appropriate real-time performance. Fast optimization algorithms also enable implementation with inexpensive, commodity hardware, thereby enabling widespread deployment. The research team is leveraging Argonne’s expertise in numerical optimization to deploy state-of-the-art and open-source optimization functionality and developing model reformulations and warm-starting strategies. These capabilities will enable set-point updates in a few minutes for large and highly detailed building models and long forecast horizons, enhancing system responsiveness and robustness.

The algorithms are being implemented in BuildingIQ’s system, which uses building models constructed automatically from sensor data and machine learning techniques. This approach avoids the need for expensive model development tasks and simulation engines that limit deployment.

A proactive EM system is currently in use in the Theory and Computational Science Building on the Argonne campus. A new generation of occupancy sensors, developed by global technology innovator Johnson Controls, will be field-tested as part of this demonstration project. Argonne and BuildingIQ will add new data mining capabilities to allow for association of occupancy patterns with building zones or other items of interest, while maintaining confidentiality of occupant identities.

This project is being funded by U.S. Department of Energy’s Building Technologies Program.